Climate and Sustainability Hackathon with Cloudera and AMD

A carbon stock is the quantity of carbon contained in a “pool,” meaning a reservoir or system which has the capacity to accumulate or release carbon. In the context of forests, for example, this refers to the amount of carbon stored in the world’s forest ecosystem — primarily in living biomass and soil, but to a lesser extent also in dead wood and litter.

While critical to climate mitigation, carbon stock calculations can be inaccurate for a variety of reasons. Those reasons include:

• Variability in carbon content: accurate measurements require comprehensive sampling and data collection which is resource-intensive
• Incomplete data
• Spatial and temporal variability
• Measurement techniques
• Scale and resolution
• Human error
• Assumptions and oversimplification
• Uncertainty in models
• Data availability and quality

How can we use machine learning methods to more accurately measure and utilize carbon stock?

Possible datasets:

• NASA’s EarthData
• World Resource Institute
• Edgar Database

Using machine learning methods to advance climate-smart agriculture (CSA) is essential for addressing global hunger and mitigating the climate crisis. Here are some potential projects and considerations for leveraging machine learning in CSA:

• Climate modeling and prediction
• Crop yield prediction
• Pest and disease detection
• Irrigation management
• Precision agriculture
• Soil health assessment
• Crop selection and rotation
• Carbon sequestration
• Supply chain optimization
• Decision support systems
• Climate adaptation strategies
• Data-driven research

To support these projects, consider utilizing the following datasets:

• FAOSTAT
• WorldClim
• NASS
• Sentinel-Hub

Machine learning can empower farmers, researchers, and policymakers to make informed decisions, optimize agricultural practices, and address the challenges of food security and climate change.

While water is something many take for granted, its scarcity is becoming one of the most pressing sustainability challenges for businesses, governments, communities, and individuals around the world. Besides being fundamental to sustaining life, water also is integral for agriculture, manufacturing, and industrial processes.

The climate crisis is a water crisis, too. As the planet warms, this leads to increased evaporation, changing and unpredictable precipitation patterns, rising sea levels, and melting snow pack and glaciers, among other challenges. Addressing water scarcity is becoming a critical issue.

Possible projects include:

• Forecasting water consumption based on historical data, weather data, and population growth
• Using satellite imagery to detect changes in the environment that might indicate underground leaks in large pipelines
• Predicting the amount of rainwater that can be harvested in specific regions based on weather forecasts and historical data to aid in designing effective rainwater harvesting systems.

Possible Datasets:

• USGS Water Data
• GRACE
• SMAP
• GRanD

Cities are responsible for 70 percent of global greenhouse gas emissions. That means that the climate crisis will be won or lost in our urban environments. Many of these emissions are driven by industrial and transportation systems reliant on fossil fuels.

But machine learning and big data offer promise for developing the smart cities of tomorrow. By improving efficiencies and enabling better decision-making, we can address the sustainability challenges afflicting cities around the world. 

In this challenge, participants will apply machine learning to an urban sustainability challenge to create long-lasting solutions.

Possible projects include:

• Air quality prediction and monitoring
• Predicting energy demand in different parts of the city to optimize electricity distribution
• Using imagery to classify waste types for more efficient recycling processes

Possible Datasets:

• UN Habitat
• Building Performance Database
• Uber Movement

These are just a few ideas, if you have your own idea for a machine learning project using publicly available data that is focused on climate and sustainability issues, then we would love for you to submit your own idea!